IoT-O, a Core-Domain IoT Ontology to Represent Connected Devices Networks

International audienceSmart objects are now present in our everyday lives, and the Internet of Things is expanding both in number of devices and in volume of produced data. These devices are deployed in dynamic ecosystems , with spatial mobility constraints, intermittent network availability depending on many parameters (e.g. battery level or duty cycle), etc. To capture knowledge describing such evolving systems, open, shared and dynamic knowledge representations are required. These representations should also have the ability to adapt over time to the changing state of the world. That is why we propose IoT-O, a core-domain modular IoT ontology proposing a vocabulary to describe connected devices and their relation with their environment. First, existing IoT ontologies are described and compared to requirements an IoT ontology should be compliant with. Then, after a detailed description of its modules, IoT-O is instantiated in a home automation use case to illustrate how it supports the description of evolving systems

A de-verticalizing middleware for IoT systems based on information centric networking design

The Internet of Things is rapidly diffusing and many stand-alone platforms have been deployed in different domains. Since these solutions are still isolated, the definition of a globally unified platform embracing different and heterogeneous Internet of Things systems is gaining momentum. In the Internet Research Task Force context, the Information-Centric Networking Research Group envisages the possibility to reach this challenging goal by properly leveraging the communication primitives of the Information-Centric Networking paradigm. In line with this vision, this contribution proposes a concrete solution that offers: name-based communication scheme, flexible data delivery, support for heterogeneous network infrastructures, platform interoperability, and technology-independent implementation of high-level applications. Moreover, a proof-of-concept implementation is presented for further describing the main functionalities of the designed approach and demonstrating its correct execution in a real environment

Towards multi-layer interoperability of heterogeneous IoT platforms:the INTER-IoT approach

\u3cp\u3eOpen interoperability delivers on the promise of enabling vendors and developers to interact and interoperate, without interfering with anyone’s ability to compete by delivering a superior product and experience. In the absence of global IoT standards, the INTER-IoT voluntary approach will support and make it easy for any IoT stakeholder to design open IoT devices, smart objects, services, and complex systems and get them to be operative and interconnected quickly, thus creating new IoT interoperable ecosystems by using a bottom-up approach. In particular, INTER-IoT is based on hardware/software tools (INTER-Layer) granting multi-layer interoperability among IoT system layers (i.e. device, networking, middleware, application service, data and semantics), on frameworks for open IoT application and system programming and deployment (INTER-FW), and on a full-fledged CASE tool-supported engineering methodology for IoT systems integration (INTER-Meth). The INTER-IoT approach is notably exemplified through two use cases: INTER-LogP, involving interoperability of port logistics ecosystems, and INTER-Health, encompassing integration between e-Health at home and in mobility infrastructures.\u3c/p\u3

Big data, IoT and semantics

Big data and the Internet of things are two parallel universes, but they are so close that in most cases they blend together. The amount of devices that connect to the internet grows day by day and they bring millions of data. The IoT generates unprecedented amounts of data and this impacts on the entire big data universe. The IoT and big data are clearly growing apace, and are set to transform many areas of business and everyday life. Semantic technologies play a fundamental role in reducing incompatibilities among data formats and providing an additional layer on which applications can be built, to reason over data and extract new meaningful information. In this chapter we report the most common approaches adopted in dealing with Big Data and IoT problems and explore some of the semantic based solutions which address such problematics

Towards Big Data Analytics in Large-Scale Federations of Semantically Heterogeneous IoT Platforms

Part 1: SEDSEALInternational audienceThe technological advances in the Internet-of-Things (IoT) have led to the generation of large amounts of data and the production of a large number of IoT platforms for their management. The abundance of raw data necessitates the use of data analytics in order to extract useful patterns for decision making. Current architectures for big data analytics in the IoT domain address the large volume and velocity of the produced data. However, they do not address the semantic heterogeneity in the data models used by diverse IoT platforms, which emerges when large-scale deployments, spanning across multiple deployment sites, are considered. This paper proposes an architecture for big data analytics in the context of large-scale IoT systems consisting of multiple IoT platforms. A Semantic Interoperability Layer (SIL) handles the interoperability among the data models of the individual platforms, using semantic mappings between them and a unified ontology. Data queries to the SIL and result collection is handled by a cloud-based data management layer, namely the Data Lake, along with storage of metadata needed by data analytics methods. Based on this infrastructure, web-based data analytics and visual analytics methods are used to analyze the collected data, while being agnostic of platform-specific details. The proposed architecture is developed in the context of healthcare provision for older people, although it can be applied to any IoT domain

Semantic Middleware Architectures for IoT Healthcare Applications

International audienceThe adoption of the Internet of Things (IoT) in healthcare has received considerable interest in the past decade. Indeed, IoT-based solutions are poised to transform how we keep people safe and healthy especially as the demand for solutions to lower healthcare costs increases in the coming years. However, the heterogeneity of the things that can be connected in such environments makes interoperability among them a challenging problem. Moreover, the observations produced by these things are made available with various vocabularies and data formats. This heterogeneity prevents generic solutions from being adopted on a global scale and makes difficult to share and reuse data for other purposes than those for which they were initially set up. In this book chapter, we provide an overview of the different solutions from both technical and semantic perspectives that have been used recently to tackle the interoperability issue in such IoT environments and especially in healthcare domain. We also present an overview of semantic middleware solutions that have combined the technical and semantic techniques for a complete interoperable solution